Assessing the Customer Experience at the Intersection of Sustainable and Fine Dining: A Sentiment Analysis of MICHELIN’s 3-Star and Green-Starred Restaurants in the USA
Year: 2025
Type: article
Abstract: This study examines customer satisfaction at MICHELIN Green-star restaurants in the USA through Google Reviews using a big-data approach. Using KH Coder for text mining, the study combines frequency analysis, co-occurrence networks, topic modeling (LDA), and sentiment classification to identify key satisfaction drivers. Results show that experience, food, service, and value dominate reviews, while topic modeling reveals five latent dimensions: Restaurant, Atmosphere, Service, Experience, and Food. Sentiment analysis highlights a spectrum from dissatisfaction with waiting times to strong satisfaction, reflecting the overall experience. Findings indicate that satisfaction in sustainable luxury eateries is multidimensional, encompassing sensory, emotional, and structural aspects. Practically, while sustainability enhances reputation, satisfaction is mainly driven by excellence in food, service, and ambiance, emphasizing consistent delivery across core experiential touchpoints.
Authors: Nga Thi Hong Nguyen, Dat Hung Ho, Hak-Seon Kim
Language: English
Topic: Culinary Culture and Tourism
Subfield: Food Science
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7122488952
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